CN116339387A - Unmanned aerial vehicle safety distance maintaining method under influence of complex turbulence in narrow space - Google Patents

Unmanned aerial vehicle safety distance maintaining method under influence of complex turbulence in narrow space Download PDF

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CN116339387A
CN116339387A CN202310597276.1A CN202310597276A CN116339387A CN 116339387 A CN116339387 A CN 116339387A CN 202310597276 A CN202310597276 A CN 202310597276A CN 116339387 A CN116339387 A CN 116339387A
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aerial vehicle
unmanned aerial
turbulence
safety
model
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CN116339387B (en
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郭克信
范大东
余翔
郭雷
王建梁
张晓莉
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Xianheng International Hangzhou Aviation Automation Co ltd
Hangzhou Innovation Research Institute of Beihang University
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Xianheng International Hangzhou Aviation Automation Co ltd
Hangzhou Innovation Research Institute of Beihang University
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Abstract

The invention provides a method for keeping the safety distance of an unmanned aerial vehicle under the influence of complex turbulence in a narrow space, which aims to solve the problem that the safety obstacle avoidance is influenced by air flow interference when the unmanned aerial vehicle executes an operation task in the narrow space, and comprises the following steps: firstly, carrying out deep coupling modeling of a turbulent flow effect in a narrow space; then, an unmanned plane motion and dynamics model containing airflow disturbance is constructed, and a turbulence observer is designed according to the deep coupling interference model; secondly, establishing a safe flight corridor of the unmanned aerial vehicle, and converting Euclidean distance constraint into safety constraint related to lifting force of the unmanned aerial vehicle based on a control obstacle function theory and an unmanned aerial vehicle model with disturbance information; and finally, designing a nonlinear model prediction controller aiming at the track tracking performance and the safety obstacle avoidance requirement of the unmanned aerial vehicle, and solving the optimal expected lift force and angular velocity of the unmanned aerial vehicle in a future flight time. The invention can obviously improve the autonomous safety obstacle avoidance performance of the unmanned aerial vehicle under complex interference, and can be used for special operation tasks of underground comprehensive pipe racks and electric power tunnels.

Description

Unmanned aerial vehicle safety distance maintaining method under influence of complex turbulence in narrow space
Technical Field
The invention belongs to the field of special operation of flying robots, and particularly relates to a method for keeping a safe distance of an unmanned aerial vehicle under the influence of complex turbulence in a narrow space, which is suitable for an unmanned aerial vehicle control system which needs to execute inspection tasks such as an underground comprehensive pipe gallery, an electric power tunnel and the like.
Background
With the increasing speed of urban transformation, the overground space part of the city can not completely meet the development requirement of the city, so the full development and utilization of the underground space of the city are particularly critical. As an important foundation for intelligent city construction, in recent years, underground utility tunnel construction in China is developing at a high speed. Underground pipe galleries are generally hidden outside the line of sight of citizens, and if dangerous situations occur like 'thrombosis' of cities, great inconvenience and loss are brought to urban residents.
In order to better manage and maintain the comprehensive pipe rack, regular inspection needs to be carried out on the comprehensive pipe rack, the problems are found and solved in time, and the normal operation of the comprehensive pipe rack is ensured. The traditional inspection mode mainly comprises manual inspection, fixed point position sensor monitoring and ground robot inspection. The manual inspection frequency is low, and the underground environment is particularly severe, and the environment with high temperature, high humidity and oxygen deficiency brings great harm to the life and health of workers. The fixed monitoring equipment is influenced by factors such as piping lane spatial layout, facility equipment and the like, and the problems that detection blind areas are difficult to eliminate, false alarm and missing alarm phenomena are excessive and the like exist. The underground inspection robot is flexible to move, but cannot detect high-altitude targets. The underground infrastructure environment is faint, satellite signals are naturally refused, and the underground inspection robot is difficult to perceive and position. And the underground comprehensive pipe rack has a plurality of irregular narrow channels, so that the underground robot is difficult to cross over, and the application range of the underground robot is greatly limited. In general, the manual inspection potential safety hazard is high, the report missing rate of the fixed monitoring equipment is high, the underground robot has poor trafficability, and the requirements of autonomous, efficient and safe inspection in the underground comprehensive pipe gallery are difficult to adapt.
Aiming at the bottleneck problems of 'people can not reach, the machine can not reach' and the like in the inspection of the utility tunnel, the use of an unmanned aerial vehicle for the inspection of the utility tunnel has become a new state of management of modern urban infrastructure, and has obvious advantages in the aspects of inspection efficiency, safety, precision and the like. The utility tunnel often space is all very narrow and small, and when unmanned aerial vehicle carries out safe area planning, physical constraint is stronger to unmanned aerial vehicle is when wherein flying, is extremely easily influenced by air current disturbance such as ground effect, ceiling effect, and this has put forward higher requirement to unmanned aerial vehicle's orbit planning and control. In order to further enhance the safety of the unmanned aerial vehicle in the operation task, the aerial flying robot, particularly the inspection unmanned aerial vehicle, has the autonomous obstacle avoidance capability under the influence of limited physical space constraint and complex turbulence disturbance, and the tasks such as autonomous inspection and the like are completed. The unmanned aerial vehicle planning and control algorithm must solve the above mentioned problem of the influence of strong physical constraint and composite interference on unmanned aerial vehicle trajectory tracking and safe distance maintenance in the design process.
Chinese patent application CN202110367278.2 proposes an underground piping lane inspection method based on unmanned aerial vehicle, but there are two problems: (1) The autonomous safety obstacle avoidance function of the unmanned aerial vehicle is not considered in the inspection process; (2) Air flow disturbance generated by interaction of the unmanned aerial vehicle and the underground pipe gallery is not considered; chinese patent application CN201910242470.1 proposes a system for inspecting underground utility tunnel, but there are two similar problems: (1) Generating safety obstacle avoidance constraint in real time according to the established map; (2) The complex airflow in a narrow space is not modeled, and the influence of the complex interference is considered in unmanned plane planning and control; the Chinese patent application CN202210346957.6 proposes an autonomous navigation method and an autonomous navigation system of a rotor unmanned aerial vehicle in a tunnel environment, but a base station is required to be arranged in the tunnel in advance for positioning the unmanned aerial vehicle, and the autonomous navigation system does not have an autonomous safety obstacle avoidance function; chinese patent application CN201910185232.1 proposes a cable tunnel inspection flight method for unmanned aerial vehicles, but in the method, unmanned aerial vehicles can only carry out line inspection flight, and have no functions of autonomous drawing construction and obstacle avoidance.
Therefore, the method does not consider the problem of keeping the safety distance of the unmanned aerial vehicle under the condition of complex turbulence interference, so as to complete the task of autonomous inspection operation with high difficulty.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an unmanned aerial vehicle autonomous safety distance keeping method under the influence of complex turbulence in a narrow space for an unmanned aerial vehicle system for autonomous inspection operation. The method can ensure the safety of operation of the unmanned aerial vehicle in a limited space and improve the autonomous obstacle avoidance capability of the unmanned aerial vehicle under the disturbance condition.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for keeping the safe distance of an unmanned aerial vehicle under the influence of complex turbulence in a narrow space comprises the following steps:
firstly, carrying out deep coupling modeling of turbulence interference of an underground space to realize a turbulence effect model of deep coupling;
secondly, establishing an unmanned plane kinematics and dynamics model containing a turbulence effect, and designing a turbulence observer to estimate interference;
thirdly, establishing a safe flight corridor according to a real-time three-dimensional map, and converting the safe flight corridor into a safety constraint related to the lift force of the unmanned aerial vehicle based on a high-fidelity unmanned aerial vehicle model;
and fourthly, designing a nonlinear model predictive controller, and solving the optimal control quantity of the unmanned aerial vehicle which meets the track tracking and safety obstacle avoidance requirements for a period of time in the future.
Further, the first step includes:
for a rotor of a four-rotor unmanned aerial vehicle, the rotor is in a region not affected by aerodynamic effects and has a rotating speed of
Figure SMS_1
When the lift force is generated is +.>
Figure SMS_2
,/>
Figure SMS_3
For lift coefficient>
Figure SMS_4
The number of the rotor wing is given. When the single rotor is in the area affected by the turbulence effect, the lift force +.>
Figure SMS_5
The rotor wing is changed along with the height from the ground, specifically:
Figure SMS_6
，
wherein ,
Figure SMS_7
for the height of the rotor from the ground, +.>
Figure SMS_8
Is the radius of the propeller;
thus the total lift of the quadrotor unmanned aerial vehicle
Figure SMS_9
The disturbing external force of the turbulent flow effect is:
Figure SMS_10
,
suppose that the unmanned aerial vehicle is at pitch angle
Figure SMS_11
Forward flight, the turbulence effect is applied at this time to interfere with external force:
Figure SMS_12
，
wherein ,
Figure SMS_13
for unmanned aerial vehicle range altitude, +.>
Figure SMS_14
Is the length between the rotor and the geometric center of the unmanned aerial vehicle.
Further, the second step includes:
the turbulence is considered to generate interference external force influence on the unmanned aerial vehicle, and the model form of the unmanned aerial vehicle is established as follows:
Figure SMS_15
，
wherein ,
Figure SMS_19
indicating the position of the drone->
Figure SMS_28
Representation->
Figure SMS_31
Derivative (F)>
Figure SMS_18
Representing the speed of the unmanned aerial vehicle, +.>
Figure SMS_27
Representation->
Figure SMS_35
Derivative (F)>
Figure SMS_39
Is the total weight of the inspection unmanned aerial vehicle, < +.>
Figure SMS_21
Represents the thrust vector under the unmanned aerial vehicle body coordinate system, < ->
Figure SMS_29
Representing the total thrust of the unmanned aerial vehicle motor, +.>
Figure SMS_34
Is unmanned plane attitude rotation matrix representation form, +.>
Figure SMS_38
Representation->
Figure SMS_20
Derivative (F)>
Figure SMS_25
Indicating the acceleration of gravity>
Figure SMS_32
Represents the angular velocity of the unmanned aerial vehicle, +.>
Figure SMS_36
Indicating angular velocity +.>
Figure SMS_22
Is an antisymmetric matrix of>
Figure SMS_26
Representation->
Figure SMS_33
Derivative (F)>
Figure SMS_37
Representing the total torque produced by the unmanned aerial vehicle motor, < >>
Figure SMS_16
The unit vector is represented by a vector of units,
Figure SMS_23
indicating that the unmanned aerial vehicle is disturbed by the air flow disturbance to interfere with the external force,
Figure SMS_40
，/>
Figure SMS_41
，/>
Figure SMS_17
respectively represent the components of the disturbance external force under the inertia system,
Figure SMS_24
coefficient matrix->
Figure SMS_30
Is defined as:
Figure SMS_42
，
according to the dynamics and kinematics model of the unmanned plane and the deep-coupling turbulence effect model, a turbulence observer is designed, and the form is as follows:
Figure SMS_43
，
wherein ,
Figure SMS_46
for observer gain, +.>
Figure SMS_47
Is a coefficient matrix->
Figure SMS_49
Is an intermediate auxiliary variable->
Figure SMS_45
Is->
Figure SMS_48
Estimated value of ∈10->
Figure SMS_50
Is turbulent external force->
Figure SMS_51
Estimate value->
Figure SMS_44
Is input to the controller.
Further, the third step includes:
establishing a three-dimensional terrain voxel map according to the pose estimated by the airborne visual inertial odometer and the surrounding environment depth information perceived by the depth camera; in the three-dimensional terrain voxel map, a safety flight corridor taking the unmanned aerial vehicle flight track as a reference is obtained by a reference track local point expansion method, and the safety flight corridor is a convex polyhedron, and the form of the safety flight corridor is expressed as follows:
Figure SMS_52
,
wherein ,
Figure SMS_53
and />
Figure SMS_54
Is a convex polyhedron half-space linear constraint condition, < ->
Figure SMS_55
Representing the position of the unmanned aerial vehicle;
based on the control obstacle function theory and the unmanned aerial vehicle kinematics and dynamics model including the turbulence effect established in the second step, the safety flight corridor described by the Euclidean distance between the unmanned aerial vehicle and the obstacle is converted into the safety flight corridor constraint expressed by the lifting force of the unmanned aerial vehicle, and the form is as follows:
Figure SMS_56
，
wherein ,
Figure SMS_58
representing the total thrust of the unmanned aerial vehicle motor, +.>
Figure SMS_61
Indicating the position of the unmanned aerial vehicle,
Figure SMS_62
，/>
Figure SMS_59
representing system coefficients，/>
Figure SMS_63
Safety corridor represented for geometrical constraints +.>
Figure SMS_64
Based on unmanned plane translational motion kinematic model pair +.>
Figure SMS_65
Derivative(s)>
Figure SMS_57
Based on unmanned plane translational dynamics model pair +.>
Figure SMS_60
The second derivatives were obtained and their specific forms are shown below:
Figure SMS_66
，
Figure SMS_67
，
Figure SMS_68
，
wherein ,
Figure SMS_71
express the->
Figure SMS_74
Line->
Figure SMS_75
Column elements; />
Figure SMS_70
Is the real part of the gesture quaternion, +.>
Figure SMS_72
，/>
Figure SMS_76
，/>
Figure SMS_77
The imaginary part of the gesture quaternion; />
Figure SMS_69
，/>
Figure SMS_73
，/>
Figure SMS_78
Respectively representing the velocity components of the unmanned aerial vehicle in the inertial system.
When inequality is given
Figure SMS_79
Solving a lift interval, wherein the lift interval ensures the obstacle avoidance reliability of the unmanned aerial vehicle under the influence of turbulence disturbance.
Further, the fourth step includes:
the nonlinear model predictive controller adjusts the control quantity of the unmanned aerial vehicle under the condition of meeting various constraint conditions, and ensures the unmanned aerial vehicle to avoid the obstacle safely while finishing track tracking, and the specific form is as follows:
Figure SMS_80
，
wherein ,
Figure SMS_92
respectively represent the position, the speed and the gesture of the unmanned plane,
Figure SMS_82
representing the total thrust and triaxial angular velocity of the unmanned aerial vehicle motor required for control input;
Figure SMS_96
for model predictive control cost function, +.>
Figure SMS_86
Each element of the representation vector sums squared; />
Figure SMS_93
Is a weight matrix of the process state, +.>
Figure SMS_87
For process position error, +.>
Figure SMS_90
Is a weight matrix of the process output, +.>
Figure SMS_88
Error is output for the process->
Figure SMS_94
Is a weight matrix of terminal states, +.>
Figure SMS_81
Is a terminal position error; />
Figure SMS_91
Represents the number of optimization iterations, +.>
Figure SMS_83
Representing an initial value of the state of the unmanned aerial vehicle before model predictive control optimization;
Figure SMS_95
the method comprises the steps of establishing a model of unmanned plane kinematics and dynamics comprising a turbulence effect in the second step;
Figure SMS_85
the safety constraint related to the lifting force of the unmanned aerial vehicle established in the third step is adopted; />
Figure SMS_89
and />
Figure SMS_84
Representing saturation constraints of the actuator.
Compared with the prior art, the invention has the beneficial effects that:
the unmanned aerial vehicle inspection system is mainly oriented to an unmanned aerial vehicle system for autonomous inspection in a narrow space, and compared with a traditional inspection mode, the unmanned aerial vehicle inspection system has the advantages of being wider in range and higher in efficiency. However, the reliability of obstacle avoidance of the unmanned aerial vehicle can be reduced due to kinematic interference caused by the turbulence effect in a narrow space, so that the inspection task fails. The invention is oriented to the problem of autonomous safe distance maintenance of the unmanned aerial vehicle under the influence of space limitation and pneumatic interference, and firstly, turbulent effect deep coupling modeling is carried out; then, an unmanned plane motion and dynamics model containing airflow disturbance is constructed, and a turbulence observer is designed according to the deep coupling interference model; secondly, establishing a safe flight corridor of the unmanned aerial vehicle, and converting Euclidean distance constraint into safety constraint related to lifting force of the unmanned aerial vehicle based on a control obstacle function theory and an unmanned aerial vehicle model with disturbance information; and finally, designing a nonlinear model prediction controller aiming at the track tracking performance and the safety obstacle avoidance requirement of the unmanned aerial vehicle, and solving the optimal expected lift force and angular velocity of the unmanned aerial vehicle in a future flight time. The invention can obviously improve the success rate of safety obstacle avoidance of the unmanned aerial vehicle under complex interference and ensure the quality of operation tasks.
Drawings
Fig. 1 is a flow chart of a method for maintaining a safe distance of an unmanned aerial vehicle under the influence of complex turbulence in a narrow space.
Detailed Description
Taking a general unmanned aerial vehicle inspection platform as an example to illustrate the specific implementation of the system and the method, the unmanned aerial vehicle has high requirements on the safety of the unmanned aerial vehicle when executing high-precision operation tasks in a narrow space;
as shown in fig. 1, the method for maintaining the safe distance of the unmanned aerial vehicle under the influence of complex turbulence in a small space is implemented by the following steps:
and firstly, carrying out deep coupling modeling of turbulence interference of the underground space to realize a turbulence effect model of deep coupling.
Turbulence has a significant effect on the lift generated by four rotors of a drone, one of which is exemplified by a rotor of a quad-rotor drone, which rotor is at a rotational speed in a region that is not affected by aerodynamic effects
Figure SMS_97
When the lift force is generated is +.>
Figure SMS_98
,/>
Figure SMS_99
For lift coefficient>
Figure SMS_100
The number of the rotor wing is given. When the single rotor is in the area affected by the turbulence effect, the lift force +.>
Figure SMS_101
The rotor wing is changed along with the height from the ground, specifically:
Figure SMS_102
，
wherein ,
Figure SMS_103
for the height of the rotor from the ground, +.>
Figure SMS_104
For the radius of the propeller, the rotor is,
thus the total lift of the quadrotor unmanned aerial vehicle
Figure SMS_105
The disturbing external force of the turbulent flow effect is:
Figure SMS_106
，
suppose that the unmanned aerial vehicle is at pitch angle
Figure SMS_107
Forward flight, the turbulence effect is applied at this time to interfere with external force:
Figure SMS_108
，
wherein ,
Figure SMS_109
for unmanned aerial vehicle range altitude, +.>
Figure SMS_110
Is the length between the rotor and the geometric center of the unmanned aerial vehicle. The pneumatic corresponding model comprises the unmanned aerial vehicle state such as the height of the unmanned aerial vehicle from the ground, the pitch angle of the unmanned aerial vehicle when the unmanned aerial vehicle flies forward, and the like. The built aerodynamic effect model is a deep coupling interference model.
And secondly, establishing a unmanned plane kinematics and dynamics model containing turbulence effect, and designing a turbulence observer to estimate interference.
Under the influence of turbulence effect, the traditional unmanned aerial vehicle kinematics and dynamics model needs to be further improved, the influence of turbulence on external interference force generated by the unmanned aerial vehicle is considered, and the established unmanned aerial vehicle model is as follows:
Figure SMS_111
，
wherein ,
Figure SMS_118
indicating the position of the drone->
Figure SMS_128
Representation->
Figure SMS_129
Derivative (F)>
Figure SMS_117
Representing the speed of the unmanned aerial vehicle, +.>
Figure SMS_119
Representation->
Figure SMS_122
Derivative (F)>
Figure SMS_124
Is the total weight of the inspection unmanned aerial vehicle, < +.>
Figure SMS_114
Represents the thrust vector under the unmanned aerial vehicle body coordinate system, < ->
Figure SMS_127
Representing the total thrust of the unmanned aerial vehicle motor, +.>
Figure SMS_136
Is unmanned plane attitude rotation matrix representation form, +.>
Figure SMS_137
Representation->
Figure SMS_113
Derivative (F)>
Figure SMS_130
Indicating the acceleration of gravity>
Figure SMS_132
Represents the angular velocity of the unmanned aerial vehicle, +.>
Figure SMS_134
Indicating angular velocity +.>
Figure SMS_116
Is an antisymmetric matrix of>
Figure SMS_120
Representation->
Figure SMS_123
Derivative (F)>
Figure SMS_126
Representing the total torque produced by the unmanned aerial vehicle motor, < >>
Figure SMS_112
The unit vector is represented by a vector of units,
Figure SMS_125
indicating that the unmanned aerial vehicle is disturbed by the air flow disturbance to interfere with the external force,
Figure SMS_133
，/>
Figure SMS_135
，/>
Figure SMS_115
respectively represent the components of the disturbance external force under the inertia system,
Figure SMS_121
coefficient matrix->
Figure SMS_131
Is defined as:
Figure SMS_138
，
according to the dynamics and kinematics model of the unmanned plane and the deep-coupling turbulence effect model, a turbulence observer is designed, and the form is as follows:
Figure SMS_139
，
wherein ,
Figure SMS_142
for observer gain, +.>
Figure SMS_143
Is a coefficient matrix->
Figure SMS_146
As an intermediate auxiliary variable, a variable is provided,
Figure SMS_141
is->
Figure SMS_144
Estimated value of ∈10->
Figure SMS_145
Is turbulent external force->
Figure SMS_147
Estimate value->
Figure SMS_140
Is input to the controller.
And thirdly, establishing a safe flight corridor according to the real-time three-dimensional map, and converting the safe flight corridor into a safety constraint related to the lift force of the unmanned aerial vehicle based on the high-fidelity unmanned aerial vehicle model.
Establishing a three-dimensional terrain voxel map according to the pose estimated by the airborne visual inertial odometer and the surrounding environment depth information perceived by the depth camera; in the three-dimensional terrain voxel map, a safety flight corridor taking the unmanned aerial vehicle flight track as a reference is obtained by a reference track local point expansion method, and the safety flight corridor is a convex polyhedron, and the form of the safety flight corridor is expressed as follows:
Figure SMS_148
，
wherein ,
Figure SMS_149
and />
Figure SMS_150
Is a convex polyhedron half-space linear constraint condition, < ->
Figure SMS_151
Indicating the position of the drone.
The safety flight corridor represents the Euclidean distance constraint between the position of the unmanned aerial vehicle and surrounding obstacles, and the geometric constraint plays a role in constraint only when the unmanned aerial vehicle approaches to the boundary of the safety flight corridor, so that the safety flight corridor has conservation to the safety obstacle avoidance of the unmanned aerial vehicle. In addition, the safety boundary of the unmanned aerial vehicle in the flight process needs to be dynamically adjusted along with the interference, and the geometrical constraint lacks quantification of the influence of turbulence disturbance on the safety boundary. In order to overcome the difficulty, based on the theory of the control barrier function and the unmanned aerial vehicle kinematics and dynamics model comprising the turbulence effect established in the second step, the safety flight corridor described by the Euclidean distance between the unmanned aerial vehicle and the barrier is converted into the safety flight corridor constraint expressed by the lifting force of the unmanned aerial vehicle, and the safety flight corridor constraint is formed as follows:
Figure SMS_152
，
wherein ,
Figure SMS_155
representing the total thrust of the unmanned aerial vehicle motor, +.>
Figure SMS_159
Indicating the position of the drone->
Figure SMS_161
，/>
Figure SMS_154
Representing system coefficients>
Figure SMS_157
Safety corridor represented for geometrical constraints +.>
Figure SMS_158
Based on unmanned plane translational motion kinematic model pair +.>
Figure SMS_160
Derivative(s)>
Figure SMS_153
Based on unmanned plane translational dynamics model pair +.>
Figure SMS_156
The second derivatives were obtained and their specific forms are shown below:
Figure SMS_162
,
Figure SMS_163
，
Figure SMS_164
，
wherein ,
Figure SMS_166
express the->
Figure SMS_168
Line->
Figure SMS_170
Column elements. />
Figure SMS_167
Is the real part of the gesture quaternion, +.>
Figure SMS_169
，/>
Figure SMS_171
，/>
Figure SMS_172
Is the imaginary part of the gesture quaternion. />
Figure SMS_165
，/>
Figure SMS_173
，/>
Figure SMS_174
Respectively representing the velocity components of the unmanned aerial vehicle in the inertial system.
When inequality is given
Figure SMS_175
Solving a lift interval, wherein the lift interval ensures the obstacle avoidance reliability of the unmanned aerial vehicle under the influence of turbulence disturbance.
And fourthly, designing a nonlinear model predictive controller, and solving the optimal control quantity of the unmanned aerial vehicle which meets the track tracking and safety obstacle avoidance requirements for a period of time in the future.
When the unmanned aerial vehicle works in a narrow space, the unmanned aerial vehicle is required to track the reference track points solved by the global path planning module, and meanwhile, safety constraint required by obstacle avoidance in the flight process is met. The nonlinear model predictive controller can generate control quantity of the unmanned aerial vehicle under various constraint conditions, and ensure safety obstacle avoidance of the unmanned aerial vehicle while finishing track tracking. The specific form is as follows:
Figure SMS_176
，
wherein ,
Figure SMS_184
respectively representing the position, speed and attitude of the unmanned plane, < ->
Figure SMS_179
Representing the total thrust and triaxial angular velocity of the unmanned aerial vehicle motor required for control input;
Figure SMS_187
for model predictive control cost function, +.>
Figure SMS_182
Representing the sum of squares of the elements of the vector. />
Figure SMS_191
Is a weight matrix of the process state, +.>
Figure SMS_186
For process position error, +.>
Figure SMS_189
Is a weight matrix of the process output, +.>
Figure SMS_181
Error is output for the process->
Figure SMS_192
Is a weight matrix of terminal states, +.>
Figure SMS_177
Is a terminal position error; />
Figure SMS_188
Represents the number of optimization iterations, +.>
Figure SMS_180
Representing an initial value of the state of the unmanned aerial vehicle before model predictive control optimization;
Figure SMS_190
the method comprises the steps of establishing a model of unmanned plane kinematics and dynamics comprising a turbulence effect in the second step;
Figure SMS_178
the safety constraint related to the lifting force of the unmanned aerial vehicle established in the third step is adopted; />
Figure SMS_185
and />
Figure SMS_183
Representing saturation constraints of the actuator.
What is not described in detail in the present specification belongs to the prior art known to those skilled in the art.

Claims (5)

1. The unmanned aerial vehicle safety distance maintaining method under the influence of complex turbulence in a narrow space is characterized by comprising the following steps of:
firstly, carrying out deep coupling modeling of turbulence interference of an underground space to realize a turbulence effect model of deep coupling;
secondly, establishing an unmanned plane kinematics and dynamics model containing a turbulence effect, and designing a turbulence observer to estimate interference;
thirdly, establishing a safe flight corridor according to a real-time three-dimensional map, and converting the safe flight corridor into a safety constraint related to the lift force of the unmanned aerial vehicle based on a high-fidelity unmanned aerial vehicle model;
and fourthly, designing a nonlinear model predictive controller, and solving the optimal control quantity of the unmanned aerial vehicle which meets the track tracking and safety obstacle avoidance requirements for a period of time in the future.
2. The method for maintaining the safe distance of the unmanned aerial vehicle under the influence of complex turbulence in a small space according to claim 1, wherein the first step comprises:
for a rotor of a four-rotor unmanned aerial vehicle, the rotor is in a region not affected by aerodynamic effects and has a rotating speed of
Figure QLYQS_1
When the lift force is generated is +.>
Figure QLYQS_2
,/>
Figure QLYQS_3
For lift coefficient>
Figure QLYQS_4
For numbering the rotors, lift +.>
Figure QLYQS_5
The rotor wing is changed along with the height from the ground, specifically:
Figure QLYQS_6
，
wherein ,
Figure QLYQS_7
for the height of the rotor from the ground, +.>
Figure QLYQS_8
Is the radius of the propeller;
thus a quadrotor unmanned aerial vehicleTotal lift force
Figure QLYQS_9
The disturbing external force of the turbulent flow effect is:
Figure QLYQS_10
，
suppose that the unmanned aerial vehicle is at pitch angle
Figure QLYQS_11
Forward flight, the turbulence effect is applied at this time to interfere with external force:
Figure QLYQS_12
，
wherein ,
Figure QLYQS_13
for unmanned aerial vehicle range altitude, +.>
Figure QLYQS_14
Is the length between the rotor and the geometric center of the unmanned aerial vehicle.
3. The method for maintaining the safe distance of the unmanned aerial vehicle under the influence of complex turbulence in a small space according to claim 2, wherein the second step comprises:
the turbulence is considered to generate interference external force influence on the unmanned aerial vehicle, and the model form of the unmanned aerial vehicle is established as follows:
Figure QLYQS_15
，
wherein ,
Figure QLYQS_21
indicating the position of the drone->
Figure QLYQS_23
Representation->
Figure QLYQS_38
Derivative (F)>
Figure QLYQS_19
Representing the speed of the unmanned aerial vehicle, +.>
Figure QLYQS_25
Representation->
Figure QLYQS_32
Derivative (F)>
Figure QLYQS_36
Is the total weight of the inspection unmanned aerial vehicle, < +.>
Figure QLYQS_17
Represents the thrust vector under the unmanned aerial vehicle body coordinate system, < ->
Figure QLYQS_27
Representing the total thrust of the unmanned aerial vehicle motor, +.>
Figure QLYQS_33
Is unmanned plane attitude rotation matrix representation form, +.>
Figure QLYQS_39
Representation->
Figure QLYQS_22
Derivative (F)>
Figure QLYQS_26
Indicating the acceleration of gravity>
Figure QLYQS_40
Represents the angular velocity of the unmanned aerial vehicle, +.>
Figure QLYQS_41
Indicating angular velocity +.>
Figure QLYQS_18
Is an antisymmetric matrix of>
Figure QLYQS_28
Representation->
Figure QLYQS_30
Derivative (F)>
Figure QLYQS_35
Representing the total torque produced by the unmanned aerial vehicle motor, < >>
Figure QLYQS_16
The unit vector is represented by a vector of units,
Figure QLYQS_24
indicating the air flow disturbance external force to which the unmanned plane is subjected, < ->
Figure QLYQS_31
，
Figure QLYQS_37
，/>
Figure QLYQS_20
Respectively represent the components of the disturbance external force under the inertia system,
Figure QLYQS_29
coefficient matrix->
Figure QLYQS_34
Is defined as:
Figure QLYQS_42
，
according to the dynamics and kinematics model of the unmanned plane and the deep-coupling turbulence effect model, a turbulence observer is designed, and the form is as follows:
Figure QLYQS_43
，
wherein ,
Figure QLYQS_45
for observer gain, +.>
Figure QLYQS_50
Is a coefficient matrix->
Figure QLYQS_51
As an intermediate auxiliary variable, a variable is provided,
Figure QLYQS_46
is->
Figure QLYQS_47
Estimated value of ∈10->
Figure QLYQS_48
Is turbulent external force->
Figure QLYQS_49
The estimated value is used to determine the value of the parameter,
Figure QLYQS_44
is input to the controller.
4. A method for maintaining a safe distance of an unmanned aerial vehicle under the influence of complex turbulence in a small space according to claim 3, wherein the third step comprises:
establishing a three-dimensional terrain voxel map according to the pose estimated by the airborne visual inertial odometer and the surrounding environment depth information perceived by the depth camera; in the three-dimensional terrain voxel map, a safety flight corridor taking the unmanned aerial vehicle flight track as a reference is obtained by a reference track local point expansion method, and the safety flight corridor is a convex polyhedron, and the form of the safety flight corridor is expressed as follows:
Figure QLYQS_52
，
wherein ,
Figure QLYQS_53
and />
Figure QLYQS_54
Is a convex polyhedron half-space linear constraint condition, < ->
Figure QLYQS_55
Representing the position of the unmanned aerial vehicle;
based on the control obstacle function theory and the unmanned aerial vehicle kinematics and dynamics model including the turbulence effect established in the second step, the safety flight corridor described by the Euclidean distance between the unmanned aerial vehicle and the obstacle is converted into the safety flight corridor constraint expressed by the lifting force of the unmanned aerial vehicle, and the form is as follows:
Figure QLYQS_56
，
wherein ,
Figure QLYQS_58
representing the total thrust of the unmanned aerial vehicle motor, +.>
Figure QLYQS_62
Indicating the position of the drone->
Figure QLYQS_64
，
Figure QLYQS_59
Representing system coefficients>
Figure QLYQS_60
Is thatSafety corridor represented by geometrical constraints +.>
Figure QLYQS_63
Based on unmanned plane translational motion kinematic model pair +.>
Figure QLYQS_65
Derivative(s)>
Figure QLYQS_57
Based on unmanned plane translational dynamics model pair +.>
Figure QLYQS_61
The second derivatives were obtained and their specific forms are shown below:
Figure QLYQS_66
，
Figure QLYQS_67
，
Figure QLYQS_68
，
wherein ,
Figure QLYQS_70
express the->
Figure QLYQS_74
Line->
Figure QLYQS_75
Column elements; />
Figure QLYQS_71
Is the real part of the gesture quaternion, +.>
Figure QLYQS_73
，/>
Figure QLYQS_76
，/>
Figure QLYQS_77
The imaginary part of the gesture quaternion; />
Figure QLYQS_69
，/>
Figure QLYQS_72
，/>
Figure QLYQS_78
Respectively representing the speed components of the unmanned aerial vehicle under an inertial system;
when inequality is given
Figure QLYQS_79
Solving a lift interval, wherein the lift interval ensures the obstacle avoidance reliability of the unmanned aerial vehicle under the influence of turbulence disturbance.
5. The method for maintaining the safe distance of the unmanned aerial vehicle under the influence of complex turbulence in a small space according to claim 4, wherein the fourth step comprises:
the nonlinear model predictive controller adjusts the control quantity of the unmanned aerial vehicle under the condition of meeting various constraint conditions, and ensures the unmanned aerial vehicle to avoid the obstacle safely while finishing track tracking, and the specific form is as follows:
Figure QLYQS_80
，
wherein ,
Figure QLYQS_90
respectively representing the position, speed and attitude of the unmanned plane, < ->
Figure QLYQS_83
Representing the total thrust and triaxial angular velocity of the unmanned aerial vehicle motor required for control input;
Figure QLYQS_92
for model predictive control cost function, +.>
Figure QLYQS_88
Each element of the representation vector sums squared; />
Figure QLYQS_96
Is a weight matrix of the process state, +.>
Figure QLYQS_93
For process position error, +.>
Figure QLYQS_94
Is a weight matrix of the process output, +.>
Figure QLYQS_86
Error is output for the process->
Figure QLYQS_89
Is a weight matrix of terminal states, +.>
Figure QLYQS_81
Is a terminal position error; />
Figure QLYQS_87
Represents the number of optimization iterations, +.>
Figure QLYQS_84
Representing an initial value of the state of the unmanned aerial vehicle before model predictive control optimization;
Figure QLYQS_95
the method comprises the steps of establishing a model of unmanned plane kinematics and dynamics comprising a turbulence effect in the second step;
Figure QLYQS_85
the safety constraint related to the lifting force of the unmanned aerial vehicle established in the third step is adopted; />
Figure QLYQS_91
and />
Figure QLYQS_82
Representing saturation constraints of the actuator.
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